Suicidality emerging from rapid Venlafaxine discontinuation

There is ongoing controversy about suicide risk associated with antidepressant use, but much less is known about suicide risk related to discontinuation of antidepressants. Antidepressant withdrawal syndrome (AWS) can be a burdensome syndrome, with well-known symptoms. One possible explanation for this condition is the oppositional model of tolerance. Some study results are suggestive that suicidality can be part of AWS, although the data are still inconclusive. We are aware of only 3 case reports of suicidal behavior with antidepressant discontinuation, which occurred after discontinuation of paroxetine, venlafaxine, and escitalopram. In this case report, we present a patient without a history of suicidal crisis who, in the span of 6 months, experienced 2 suicidal crises, each occurring directly following rapid discontinuation of venlafaxine. Both crises subsided with reintroduction of the drug

Posttraumatic Stress Disorder after Vaginal Delivery at Primiparous Women

Although severe gynaecological pathology during delivery and negative outcome have been shown to be related with posttraumatic stress disorder (PTSD) little is known about traumatic experiences following regular delivery, at the expected time and with a healthy child. The objective of our study was to determine the prevalence of PTSD during postpartum period after vaginal delivery and its risk factors. The sample included 126 primiparous women. Monthly, for the next three months, the women were assessed for PTSD using the gold standard interview for PTSD, Clinician-Administered PTSD Scale (CAPS). Risk factors were assessed including sociodemographic variables, personal medical history and clinical variables. After the first month, 2.4% women had acute full PTSD and another 9.5% had clinically significant level of PTSD symptoms. Following the second and the third month, partial PTSD was found in 5.9% and 1.3% of the women, respectively, and none of participants had full PTSD. Obstetrical interventions were the only significant risk factor for the development of PTSD. Symptoms of postpartum PTSD are not rare after a traumatic delivery, and associated with specific obstetrical risk factors. Awareness of these risk factors may stimulate interventions to prevent this important and neglected postpartum disorde

Watchful waiting for depression using depathologization, advice and shared decision making

Background: Antidepressant use is on the rise, while the problem of depression on a population wide level is not being tackled. One of the hypothesis why this may be happening is the effect of adverse effects and withdrawal symptoms of antidepressants, as well as pathologization of normal sadness. Methods: In this study we did partial watchful waiting using (1) psychosocial advice; (2) depathologization; and (3) shared decision making. The study comprised of 83 consecutive non-suicidal out-patients with depressive symptoms. Results: The first among the three main findings of our study was that watchful waiting, when coupled with psychosocial advice, depathologization and shared decision making, was effective in 64.5 % of patients who ended up with no pharmaco- or psychotherapy after the three-month follow-up period. Severity of depression did not correlate with the efficacy of watchful waiting. Expectations from pharmacotherapy were the dominant factor influencing whether a patient will enter or finish the study without pharmacotherapy. Limitations: This was not a randomized control trial and the goal was to see if this approach is feasible. A randomized controlled trial comparing watchful waiting approach with pharmacotherapy could not factor in all parameters involved (i.e. self-fulfilling prophecy through pathologization, withdrawal and risk of chronicity). Conclusions: Our study showed that watchful waiting might be effective, particularly when coupled with depathologization, psychosocial advice and shared decision making, suggesting that this approach should be tested as the first-line of treatment in non-suicidal patients with depressive symptoms

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data